Towards Self-Adjustment of Adapted Pittsburgh Classifier System Cognitive Capacity on Multi-Step Problems

@article{Perournalnak2008TowardsSO,
  title={Towards Self-Adjustment of Adapted Pittsburgh Classifier System Cognitive Capacity on Multi-Step Problems},
  author={Mathias Perournalna{\"i}k and Gilles {\'E}n{\'e}e},
  journal={2008 Eighth International Conference on Hybrid Intelligent Systems},
  year={2008},
  pages={885-892}
}
This paper focuses on the study of the influence of a newly implemented mechanism on a Pittsburgh-like classifier system. The Adapted Pittsburgh Classifier System is a learning classifier system that uses genetic algorithms to evolve its ruleset. The new mechanism discussed is inspired from Wilson work on the eXtended Classifier System (XCS): it allows the concerned LCS to adapt its rule set when facing a new signal by modifying an existing rule. This mechanism is called covering mechanism due… CONTINUE READING